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Journal of NeuroEngineering and Rehabilitation

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Open Access 01-12-2019 | Research

Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks

Authors: Emilio Trigili, Lorenzo Grazi, Simona Crea, Alessandro Accogli, Jacopo Carpaneto, Silvestro Micera, Nicola Vitiello, Alessandro Panarese

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2019

Abstract

Background

To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities.

Methods

Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Go-backward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method.

Results

The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications.

Conclusions

The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.

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Title: Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks
Authors: Emilio Trigili
Lorenzo Grazi
Simona Crea
Alessandro Accogli
Jacopo Carpaneto
Silvestro Micera
Nicola Vitiello
Alessandro Panarese
Publication date: 01-12-2019
Publisher: BioMed Central
Published in: Journal of NeuroEngineering and Rehabilitation / Issue 1/2019
Electronic ISSN: 1743-0003
DOI: https://doi.org/10.1186/s12984-019-0512-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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